Multi-Gene Genetic Programming for Short Term Load Forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Ghareeb:2013:EPECS,

• author = "W. T. Ghareeb and E. F. {El Saadany}",
• booktitle = "3rd International Conference on Electric Power and Energy Conversion Systems (EPECS 2013)",
• title = "Multi-Gene Genetic Programming for Short Term Load Forecasting",
• year = "2013",
• month = "2-4 " # oct,
• keywords = "genetic algorithms, genetic programming, Short-term load forecasting, multi-gene genetic programming, radial basis function",
• DOI = "doi:10.1109/EPECS.2013.6713061",
• abstract = "The Short Term Load Forecasting (STLF) plays a critical role in power system operation. The accuracy of the STLF is very important since it affects the generation scheduling and the electricity prices and hence an accurate STLF method should be used. This paper presents a new variant of genetic programming namely: Multi-Gene Genetic Programming (MGGP) for the problem of STLF. In order to demonstrate this technique capability, the MGGP has been compared with the RBF network and the standard single-gene Genetic Programming (GP) in terms of the forecasting accuracy. The data used in this study is a real data set of the Egyptian electrical network. The weather factors represented by the minimum and the maximum daily temperature have been included in this study. The MGGP has successfully predicted the future load with high accuracy compared to that of the Radial Basis Function (RBF) network and that of the standard single-gene Genetic Programming (GP).",
• notes = "Also known as \cite{6713061}",

}

Genetic Programming entries for Wael Taha Ghareeb Ehab El-Saadany

Citations